Hom-e-call - An enhanced fall detection system based on accelerometer and optical sensors applicable in domestic environment

A reliable detection of falls is an important challenge for applications of ambient assisted living. Especially for elderly people living autonomously at their home, such a system would help to treat possible injuries faster. Most of the currently used systems are only based on the analysis of the movement data recorded by a wearable device. In this study an automatic fall detection system is developed that can be installed in domestic environment. Based on an accelerometer fixed to the body and optical sensors mounted on the wall, the fusion unit analyses the actual posture of the patient and detects fall events. By means of simulated fall scenarios performed by young athletes, the overall system performance is tested. To identify the false alarm rate of the fall detection system elderly people have performed activities of daily living in a furnished test flat. The combined system approach can be used to improve conventional fall detection algorithms.